HOG features were used to represent images in a small pathology image dataset, as a traditional computer vision approach, to see how it compares to deep learning. Deep learning can also be used for automatic feature extraction, extracting features without manual engineering. Common feature extraction techniques include HOG, SURF, LBP, Haar wavelets, and color histograms, which reduce image dimensions into compact vectors representing important image parts.

1. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases”
The feature representation method Histogram of Oriented Gradients (HOG) are used as the feature
representation. Although deep learning approaches have proven there superiority in similar image
recognition/classification problem, given the small size of the data set it is interesting to find out how a
traditional computer vision approach performs in a situation like this.

2. Deep learning models can also be used for automatic feature extraction algorithms. Other common
feature extraction techniques include:
•Histogram of oriented gradients (HOG)
•Speeded-up robust features (SURF)
•Local binary patterns (LBP)
•Haar wavelets
•Color histograms
Feature extraction a type of dimensionality reduction that efficiently represents interesting parts of an image as a
compact feature vector. This approach is useful when image sizes are large and a reduced feature representation is
required to quickly complete tasks such as image matching and retrieval.